{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "#Below is the training settings used for ImageWoof 5 epoch, 128px.  You can kick off a run in the very next cell.\n",
    "#There are some other sample runs showing how some of the settings were adjusted...if you find better settings please let us know!"
   ]
  },
  {
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   "execution_count": 3,
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     "text": [
      "lr: 0.004; eff_lr: 0.004; size: 128; alpha: 0.99; mom: 0.95; eps: 1e-06\n",
      "/home/ubuntu/.fastai/data/imagewoof\n",
      "698  annealing start\n",
      "Using Ranger Optimizer...\n",
      "Using Ranger Optimizer...\n"
     ]
    },
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      "lr: 0.004; eff_lr: 0.004; size: 128; alpha: 0.99; mom: 0.95; eps: 1e-06\n",
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      "Using Ranger Optimizer...\n",
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      "lr: 0.004; eff_lr: 0.004; size: 128; alpha: 0.99; mom: 0.95; eps: 1e-06\n",
      "/home/ubuntu/.fastai/data/imagewoof\n",
      "698  annealing start\n",
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      "lr: 0.004; eff_lr: 0.004; size: 128; alpha: 0.99; mom: 0.95; eps: 1e-06\n",
      "/home/ubuntu/.fastai/data/imagewoof\n",
      "698  annealing start\n",
      "Using Ranger Optimizer...\n",
      "Using Ranger Optimizer...\n"
     ]
    },
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     "text": [
      "[0.734 0.75  0.748 0.732 0.738]\n",
      "0.74039996\n",
      "0.0073102703\n"
     ]
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   ],
   "source": [
    "#Run this to reproduce the results of our entry:\n",
    "%run train.py --run 5 --woof 1 --size 128 --bs 64 --mixup 0 --epoch 5 --lr 4e-3 --gpu 0 \\\n",
    "                --opt ranger --mom .95 --sched_type flat_and_anneal --ann_start 0.72"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Sample run below..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lr: 0.004; eff_lr: 0.004; size: 128; alpha: 0.99; mom: 0.95; eps: 1e-06\n",
      "/home/ubuntu/.fastai/data/imagewoof\n",
      "698  annealing start\n",
      "Using Ranger Optimizer...\n",
      "Using Ranger Optimizer...\n"
     ]
    },
    {
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      "Using Ranger Optimizer...\n",
      "Using Ranger Optimizer...\n"
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      "698  annealing start\n",
      "Using Ranger Optimizer...\n",
      "Using Ranger Optimizer...\n"
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       "      <td>00:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.406095</td>\n",
       "      <td>1.314005</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>0.968000</td>\n",
       "      <td>00:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.221502</td>\n",
       "      <td>1.167559</td>\n",
       "      <td>0.738000</td>\n",
       "      <td>0.966000</td>\n",
       "      <td>00:24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lr: 0.004; eff_lr: 0.004; size: 128; alpha: 0.99; mom: 0.95; eps: 1e-06\n",
      "/home/ubuntu/.fastai/data/imagewoof\n",
      "698  annealing start\n",
      "Using Ranger Optimizer...\n",
      "Using Ranger Optimizer...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.936900</td>\n",
       "      <td>1.887296</td>\n",
       "      <td>0.378000</td>\n",
       "      <td>0.858000</td>\n",
       "      <td>00:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.706574</td>\n",
       "      <td>1.838660</td>\n",
       "      <td>0.412000</td>\n",
       "      <td>0.866000</td>\n",
       "      <td>00:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.538803</td>\n",
       "      <td>1.465649</td>\n",
       "      <td>0.570000</td>\n",
       "      <td>0.938000</td>\n",
       "      <td>00:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.417734</td>\n",
       "      <td>1.516062</td>\n",
       "      <td>0.610000</td>\n",
       "      <td>0.938000</td>\n",
       "      <td>00:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.219298</td>\n",
       "      <td>1.146909</td>\n",
       "      <td>0.756000</td>\n",
       "      <td>0.968000</td>\n",
       "      <td>00:24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
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     "output_type": "stream",
     "text": [
      "lr: 0.004; eff_lr: 0.004; size: 128; alpha: 0.99; mom: 0.95; eps: 1e-06\n",
      "/home/ubuntu/.fastai/data/imagewoof\n",
      "698  annealing start\n",
      "Using Ranger Optimizer...\n",
      "Using Ranger Optimizer...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.984180</td>\n",
       "      <td>1.871148</td>\n",
       "      <td>0.410000</td>\n",
       "      <td>0.838000</td>\n",
       "      <td>00:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.729023</td>\n",
       "      <td>1.808504</td>\n",
       "      <td>0.416000</td>\n",
       "      <td>0.906000</td>\n",
       "      <td>00:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.579883</td>\n",
       "      <td>1.466045</td>\n",
       "      <td>0.576000</td>\n",
       "      <td>0.934000</td>\n",
       "      <td>00:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.432573</td>\n",
       "      <td>1.329645</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>0.956000</td>\n",
       "      <td>00:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.258308</td>\n",
       "      <td>1.194339</td>\n",
       "      <td>0.734000</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>00:24</td>\n",
       "    </tr>\n",
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       "</table>"
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    },
    {
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     "output_type": "stream",
     "text": [
      "[0.708 0.74  0.738 0.756 0.734]\n",
      "0.7352\n",
      "0.01552288\n"
     ]
    }
   ],
   "source": [
    "%run train.py --run 5 --woof 1 --size 128 --bs 64 --mixup 0 --epoch 5 --lr 4e-3 --gpu 0 \\\n",
    "                --opt ranger --mom .95 --sched_type flat_and_anneal --ann_start 0.72"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#possible ideas to test.....\n",
    "%run train.py --run 5 --woof 1 --size 128 --bs 64 --mixup 0 --epoch 20 --lr 3e-3 --gpu 0 \\\n",
    "                --opt ranger --sched_type flat_and_anneal --ann_start 0.7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run train.py --run 5 --woof 1 --size 128 --bs 64 --mixup 0 --epoch 20 --lr 5e-3 --gpu 0 \\\n",
    "                --opt ranger --sched_type flat_and_anneal --ann_start 0.7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run train.py --run 5 --woof 1 --size 128 --bs 64 --mixup 0 --epoch 20 --lr 8e-3 --gpu 0 \\\n",
    "                --opt ranger --sched_type flat_and_anneal --ann_start 0.7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run train.py --run 2 --woof 1 --size 128 --bs 64 --mixup 0 --epoch 5 --lr 2e-3 --gpu 0 \\\n",
    "                --opt adam --mom .95 --alpha .98 --sched_type flat_and_anneal --ann_start 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run train.py --run 2 --woof 1 --size 128 --bs 64 --mixup 0 --sa 0 --epoch 80 --lr 8e-2 --gpu 0 \\\n",
    "                --opt Adams --mom .95 --alpha .98 --sched_type flat_and_anneal --ann_start 0.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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